Systems for sharing and generating playlists are known. For example Gracenote Playlist™ by Gracenote® of Emeryville, California, offers playlist generation technology for automatically generating digital music playlists that works in offline devices, including portable MP3 players, as well as desktop applications.
Gracenote Playlist Plus™ allows a user to generate a More Like This ™playlist by selecting one or more songs, albums, or artists as seeds songs, e.g., of a song that is currently playing. Gracenote Playlist then returns a mix of music that contains music from related artists and genres. This is accomplished by Playlist Plus analyzing text data available in file tags, called metadata, and filenames of the music to link the music to an internal database of music information. Playlist Plus uses the Gracenote's proprietary metadata types, which includes a genre system that has more than 1600 individual genre categories and associated relational data. The system lets Playlist Plus find relationships between songs that may be missed by simpler systems. For example, a “Punk Pop” song may be more similar to a “Ska Revival” song than it might be to one belonging to another “Punk” sub-category, such as “Hardcore Punk.”
Last.fm Ltd. is a UK-based internet radio and music community website. Using a music recommendation system called “Audioscrobbler”, Last.fm™ builds a profile of each user's musical taste by recording details of all the songs the user listens to, either on streamed radio stations or on the user's own computer or music player. This information is transferred to Last.fm's database (“Scrobbled”) via a plugin installed into the users' music player. The profile data is displayed on the user's Last.fm profile page for others to see. The site offers numerous social networking features and can recommend and play artists similar to the user's favorites. Users can create custom radio stations and playlists from any of the audio tracks in Last.fm's music library. A user can embed a playlist in their profile page for others to listen, but the playlist needs to have at least 15 streamable tracks, each from different artists.
Similarly, U.S. Pat. No. 7,035,871 B2 entitled “Method and Apparatus for Intelligent and Automatic Preference Detection of Media Content” provides a system for listening to music online by creating a preference profile for a user. When the user signs up for the service and provides details reflecting his preferences and his play history, a preference profile is generated and stored in a preference database. The system analyses the stored profiles in the database and learns from the patterns it detects. The system recommends music to the user with attributes similar to user's play history.
Patent application publication 2006/0143236 AI entitled “Interactive Music Playlist Sharing System and Methods” describes a community media playlist sharing system, where system users upload media playlists in real-time, and which are automatically converted to a standardized format and shared with other users of the community. A playlist search interface module browses the database of media playlists and returns similar playlists of system users based on similarity of one or more of the following inputs from a system user: media identification information, media category information, media relations information, user information, or matching a plurality of media items on respective playlists. Based on the results of the playlist search interface module, the system returns a list of recommended playlists to the user.
Although conventional systems for generating playlists perform for their intended purposes, conventional systems suffer disadvantages that may render the results overbroad for the user's tastes. One disadvantage is that although conventional systems may take into account the playlists of other users, conventional systems fail to analyze the playlists of a specific group of users, and fail to consider peer group influences. For example, the music that a particular teenager listens to may be highly influenced by the music listened to by a group of the teenager's peers, such as his or her friends. A further disadvantage is that conventional systems fail to take into account the fact that the music tastes of a user may be influenced by his or her geographic location when generating playlists.